From Business Analytics to Machine Learning — One Clear Pathway
Data Analytics & Machine Learning Programmes
A four-level integrated programme — from business analytics and Power BI all the way to deep learning and AI engineering. Enter at your current level and progress at your own pace, with job placement support at Level 4.
4
Levels
110+
Hours
1,250+
Graduates
100%
In Person
Who Should Attend
A Stackable Programme for Every Stage
Enter at Level 1 or jump in at any level that matches your current background.
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Full Curriculum
Four Levels — Entry to Expert
Click any level to explore the full programme content, key tools, and career outcomes.
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Courses 1–2
Business Statistics & Data Foundations
- →Descriptive statistics and probability for business
- →Data types, distributions, and hypothesis testing
- →Reading and interpreting business data correctly
Courses 3–4
Power BI for Business Analytics
- →Connect data sources and build data models
- →Clean data and create measures
- →Build interactive dashboards and live KPI reports
Courses 5–6
Data Visualisation & Storytelling
- →Choose the right chart for every business question
- →Design executive dashboards that drive decisions
- →Live project: build a real-world analytics dashboard
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Courses 5–7
Python for Data Analysis
- →Python basics, IDEs, data Structures
- →Control Flow commands and Loops
Courses 8–9
Advanced Data Analysis with Python
- →→pandas, and NumPy for data manipulation
- →Advanced pandas: merging, reshaping, groupby operations
Courses 10–11
Advanced Data Analysis with Python
- →Advance Visualiation in Python
- Matplotlib and Seaborn for visualization
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Courses 12–13
Inferential & Predictive Statistics
- →Hypothesis testing, ANOVA, and significance
- →Probability distributions and Bayesian reasoning
- →Time series analysis and forecasting
Courses 14–15
Advanced Machine Learning
- →Regression Based Machine Learning Models
- →Model Evaluation Parameters
Course 16
End-to-End Data Science Capstone
- →Industry capstone project with real data
- →Portfolio-ready project documentation
- →Presentation to Faculty and Peers Panel
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Courses 17–20
Deep Learning & Neural Networks
- →Classification, random Forest , Logistic Regression
- →Recommender System, NLP
- →Time Series Analysis and Forecasting
Courses 25–28
MLOps & Production AI
- →Model deployment with Docker, FastAPI, and cloud
- →CI/CD for ML pipelines · Model monitoring and drift
- →Data version control and experiment tracking
Courses 29–30
Job Readiness & Placement Support
- →Portfolio building: 3 production-ready projects
- →Mock interviews Practice
- →CV review, LinkedIn optimisation, and job referral network
More Courses in This Track
Also Available in Data Science & Analytics
Standalone courses and specialist programmes in this track not covered in the four levels above.
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Join 1250 + graduates. Choose your entry level and progress at your own pace. Expert faculty, small batches
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